I'm doing Business analytics course and I refer to you video for understanding. Plz keep up the great work of enlightening us.
@UnfoldDataScience3 жыл бұрын
Thanks Sumit. Good luck with your course.
@sangeethag82283 ай бұрын
Core Point: A core point is a point that has enough neighboring points within a specified distance (called epsilon or eps). Specifically, if a point has at least min_samples points (including itself) within a distance of eps, it is considered a core point. Border Point: A border point is a point that doesn't have enough neighboring points to be a core point, but it is within the eps distance of a core point. Border points are on the edge of a cluster, but they are not dense enough to form their own core.
@faizainkorea2 жыл бұрын
Well explained
@UnfoldDataScience2 жыл бұрын
Thanks Faiza.
@vallimuthaiyah50983 жыл бұрын
Excellent Explanation!! Please upload more videos of this similar kind sir..
@UnfoldDataScience3 жыл бұрын
Thanks Valli. Sure :)
@muhammedthayyib92022 жыл бұрын
Nice and sweet explanation. I shared with my friends. Thank you Aman
@UnfoldDataScience2 жыл бұрын
Thanks Thayyib
@anifminhazkhan41433 жыл бұрын
your explanation is amazing man... keep going!
@UnfoldDataScience3 жыл бұрын
Thanks a lot.
@sumitjain16553 жыл бұрын
Again nailed the topic. This is amazing how simply you have managed to explain the the concept
@UnfoldDataScience3 жыл бұрын
Thanks Again Sumit. Please share with your friends who might get benefitted :)
@btkcodes3 жыл бұрын
Underrated Channel, Plus one sub
@UnfoldDataScience3 жыл бұрын
Thanks a lot Bala. Your words are my motivation
@Mars78222 жыл бұрын
Nice and brilliant class sir.
@sarthak_yt_20092 жыл бұрын
Really very nice teaching.....
@UnfoldDataScience2 жыл бұрын
Keep watching Sarthak
@MamoonAlRasheed Жыл бұрын
Great explanation. Thank you!
@UnfoldDataScience Жыл бұрын
Welcome
@aiuslocutius97582 жыл бұрын
Thank you for the detailed explanation!
@UnfoldDataScience2 жыл бұрын
Welcome
@luamalem26172 жыл бұрын
Thank you so much. This is clear and on point. Subscribed!
@UnfoldDataScience2 жыл бұрын
Thanks Luam 😊
@babaabba9348 Жыл бұрын
best explanation
@SuperAstrax1113 жыл бұрын
Thank you sir
@UnfoldDataScience3 жыл бұрын
Welcome Asres.
@ehsanmobinipour68258 ай бұрын
very good
@rds9815 Жыл бұрын
HI its very nice the way your explaing the topics really i love it thanks for the video
@UnfoldDataScience Жыл бұрын
You are most welcome. Pls share with friends as well
@sheruloves91903 жыл бұрын
Thanks a lot..
@UnfoldDataScience3 жыл бұрын
Welcome.
@sandipansarkar92113 жыл бұрын
FINISHED WATCHING
@rajareddypandiri22263 жыл бұрын
Excellent explanation 👌
@UnfoldDataScience3 жыл бұрын
Glad you liked it Raja.
@shubhamchoudhary54613 жыл бұрын
lucid explanation
@UnfoldDataScience3 жыл бұрын
Thanks Shubham.
@nareshjadhav49623 жыл бұрын
Excellent explanation, but one question..how can we evaluate DBSCAN , is there any test like we evaluate k- means ckuster by silhouette test?
@UnfoldDataScience3 жыл бұрын
Yes Naresh, I ll cover it in my upcoming video.
@sachinladdha6 ай бұрын
how to use DBSCAN in case of multiple features? Is there any technique to use only few features or all feature but less important with very small weightage?
@christygeorge732 жыл бұрын
Please put something for deep learning like cnns rnns and examples for those
@ravanshyam76533 жыл бұрын
noise points are not consider in any clsuters right??? if new data is added ,then that data points form a cluster around noise point and then that noise point is also includes in a cluster or not???.then accuary of algortm changes or remains constant???
@UnfoldDataScience3 жыл бұрын
Hi Ravan, Noise will not be part of any cluster in any case. There is nothing like "Accuracy" in unsupervised ML.
@ravanshyam76533 жыл бұрын
@@UnfoldDataScience thanks ❤️
@datascienceworld70413 жыл бұрын
If we give Epsilon=1 then it will randomly draw a circle on a particular data point and make its a circle with radius 1 ,so the core point is also chosen randomly ??????
@ranajaydas89063 жыл бұрын
Sir please upload a video on PCA next. 🙏
@UnfoldDataScience3 жыл бұрын
I will upload Ranajay.
@navneetgupta46693 жыл бұрын
How to select the best algorithm for the data by looking at the data? This the question that I faced in many interviews. Can you please make a video on it?
@UnfoldDataScience3 жыл бұрын
This can not be done upfront without digging deep into data however it also depends on many factors. I will explain in one video separately.
@kar21942 жыл бұрын
Hi sir, a great thanks from me. A general question sir, I have performed DBSCAN, Fuzzy, and K-means clustering, how would I suggest which algorithm is best for the data? If the dataset is quite mess, large scale 10k rows, and skewed with big amount of outliers
@surajgupta-dc2ue3 жыл бұрын
Can you pls make video on birch algorithm? Plz sir
@UnfoldDataScience3 жыл бұрын
Thanks for suggesting Suraj gupta :)
@anushamv31903 жыл бұрын
Hello sir, Which algorithm works well for customer segmentation wrt Recency, Frequency, Monetory? And is necessary to apply all the algorithms that is Kmeans, Dbscan, hier to the dataset and then come yo conclusion.
@UnfoldDataScience3 жыл бұрын
Hi Anshu, RFM is a good basic point to start with however we should try to fit data with advance techniques.
@nikhildesai24602 жыл бұрын
Hi Aman, Thanks for the explanation, but my doubt is how cluster can be decide which point needs to take as a core point? What is the math behind that?
@UnfoldDataScience2 жыл бұрын
For each point xi, compute the distance between xi and the other points. Finds all neighbor points within distance eps of the starting point (xi). Each point, with a neighbor count greater than or equal to MinPts, is marked as core point or visited.(copied from web as It was quicker)
@karthickkarthi24013 жыл бұрын
sir doubt on stats why are we converting the skewed distrubution to Gaussian distrubution?
@UnfoldDataScience3 жыл бұрын
Hi karthick, this we do typically in regression models as that is one of the assumption.
@nayanranjandas18543 жыл бұрын
Sir please upload a video on Spectral Clustering next.
@nayanranjandas18543 жыл бұрын
Sir, I want to add another point, it will be really beneficial if you make a separate video on unnormalized and normalized spectral clustering.
@UnfoldDataScience3 жыл бұрын
Sure Nayan, thanks again.
@sauravksingh3 жыл бұрын
Can you also explain Isolation FOrest
@UnfoldDataScience3 жыл бұрын
Will do Saurav. Thanks for suggesting.
@austinmark2423 жыл бұрын
Can you do a playlist on computer vision feature extraction techniques like hog sift (svm+hog), etc
@UnfoldDataScience3 жыл бұрын
Hi Augustine, I will try to add. Thanks for suggesting.
@ravanshyam76533 жыл бұрын
sir if interviewer asks differnetiate blw centroid and core point.........how can we proceed?
@UnfoldDataScience3 жыл бұрын
In DBSCAN its all about, core/border/noise points. Centriod is defined in K-means not DBscan
@SuperPhysicsgeek2 жыл бұрын
what is eps again can spell out didnt really catch the pronocuation?
@UnfoldDataScience2 жыл бұрын
Hi David, can you tell me which part of the video.
@abhinavkhandelwal10453 жыл бұрын
I have a question, which algorithm to use in varying density if not DBSCAN?